
"""simple docstring"""






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


UpperCAmelCase        =				logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def __magic_name__	(				_lowerCamelCase:		int			)					->						Optional[int]:

	'''simple docstring'''




	if "token" in model_name_or_path:
		return "rag_token"
	if "sequence" in model_name_or_path:
		return "rag_sequence"
	if "bart" in model_name_or_path:
		return "bart"
	return None




def __magic_name__	(				_lowerCamelCase:		Any,					_lowerCamelCase:		Any,					_lowerCamelCase:		Union[str, Any]			)					->						Any:

	'''simple docstring'''




	return max(metric_fn(_lowerCamelCase,					_lowerCamelCase			) for gt in ground_truths			)




def __magic_name__	(				_lowerCamelCase:		List[str],					_lowerCamelCase:		Any,					_lowerCamelCase:		int			)					->						Dict:

	'''simple docstring'''




	lowerCAmelCase												=  [line.strip() for line in open(_lowerCamelCase,					'''r'''			).readlines()]
	lowerCAmelCase												=  []

	if args.gold_data_mode == "qa":
		lowerCAmelCase												=  pd.read_csv(_lowerCamelCase,					sep='''\t''',					header=_lowerCamelCase			)
		for answer_list in data[1]:
			lowerCAmelCase												=  ast.literal_eval(_lowerCamelCase			)
			answers.append(_lowerCamelCase			)
	else:
		lowerCAmelCase												=  [line.strip() for line in open(_lowerCamelCase,					'''r'''			).readlines()]
		lowerCAmelCase												=  [[reference] for reference in references]

	lowerCAmelCase												=  lowerCAmelCase												=  lowerCAmelCase												=  0
	for prediction, ground_truths in zip(_lowerCamelCase,					_lowerCamelCase			):
		total += 1
		em += metric_max_over_ground_truths(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)
		fa += metric_max_over_ground_truths(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)

	lowerCAmelCase												=  100.0 * em / total
	lowerCAmelCase												=  100.0 * fa / total

	logger.info(F"""F1: {fa:.2f}"""			)
	logger.info(F"""EM: {em:.2f}"""			)




def __magic_name__	(				_lowerCamelCase:		Any,					_lowerCamelCase:		str,					_lowerCamelCase:		str			)					->						Dict:

	'''simple docstring'''




	lowerCAmelCase												=  args.k
	lowerCAmelCase												=  [line.strip() for line in open(_lowerCamelCase,					'''r'''			).readlines()]
	lowerCAmelCase												=  [line.strip() for line in open(_lowerCamelCase,					'''r'''			).readlines()]

	lowerCAmelCase												=  lowerCAmelCase												=  0
	for hypo, reference in zip(_lowerCamelCase,					_lowerCamelCase			):
		lowerCAmelCase												=  set(hypo.split('''\t'''			)[:k]			)
		lowerCAmelCase												=  set(reference.split('''\t'''			)			)
		total += 1
		em += len(hypo_provenance & ref_provenance			) / k

	lowerCAmelCase												=  100.0 * em / total
	logger.info(F"""Precision@{k}: {em: .2f}"""			)




def __magic_name__	(				_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		Any,					_lowerCamelCase:		List[Any]			)					->						Any:

	'''simple docstring'''




	def strip_title(_lowerCamelCase:		int			):
		if title.startswith('''"'''			):
			lowerCAmelCase												=  title[1:]
		if title.endswith('''"'''			):
			lowerCAmelCase												=  title[:-1]
		return title

	lowerCAmelCase												=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    _lowerCamelCase,					return_tensors='''pt''',					padding=_lowerCamelCase,					truncation=_lowerCamelCase,					)['''input_ids'''].to(args.device			)

	lowerCAmelCase												=  rag_model.rag.question_encoder(_lowerCamelCase			)
	lowerCAmelCase												=  question_enc_outputs[0]

	lowerCAmelCase												=  rag_model.retriever(
	    _lowerCamelCase,					question_enc_pool_output.cpu().detach().to(torch.floataa			).numpy(),					prefix=rag_model.rag.generator.config.prefix,					n_docs=rag_model.config.n_docs,					return_tensors='''pt''',					)
	lowerCAmelCase												=  rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
	lowerCAmelCase												=  []
	for docs in all_docs:
		lowerCAmelCase												=  [strip_title(_lowerCamelCase			) for title in docs['''title''']]
		provenance_strings.append('''\t'''.join(_lowerCamelCase			)			)
	return provenance_strings




def __magic_name__	(				_lowerCamelCase:		Any,					_lowerCamelCase:		Union[str, Any],					_lowerCamelCase:		Union[str, Any]			)					->						Optional[Any]:

	'''simple docstring'''




	with torch.no_grad():
		lowerCAmelCase												=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    _lowerCamelCase,					return_tensors='''pt''',					padding=_lowerCamelCase,					truncation=_lowerCamelCase			)

		lowerCAmelCase												=  inputs_dict.input_ids.to(args.device			)
		lowerCAmelCase												=  inputs_dict.attention_mask.to(args.device			)
		lowerCAmelCase												=  rag_model.generate(  # rag_model overwrites generate
		    _lowerCamelCase,					attention_mask=_lowerCamelCase,					num_beams=args.num_beams,					min_length=args.min_length,					max_length=args.max_length,					early_stopping=_lowerCamelCase,					num_return_sequences=1,					bad_words_ids=[[0, 0]],					)
		lowerCAmelCase												=  rag_model.retriever.generator_tokenizer.batch_decode(_lowerCamelCase,					skip_special_tokens=_lowerCamelCase			)

		if args.print_predictions:
			for q, a in zip(_lowerCamelCase,					_lowerCamelCase			):
				logger.info('''Q: {} - A: {}'''.format(_lowerCamelCase,					_lowerCamelCase			)			)

		return answers




def __magic_name__	(				)					->						str:

	'''simple docstring'''




	lowerCAmelCase												=  argparse.ArgumentParser()
	parser.add_argument(
	    '''--model_type''',					choices=['''rag_sequence''', '''rag_token''', '''bart'''],					type=_lowerCamelCase,					help=(
	        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
	        ''' model_name_or_path'''
	    ),					)
	parser.add_argument(
	    '''--index_name''',					default=_lowerCamelCase,					choices=['''exact''', '''compressed''', '''legacy'''],					type=_lowerCamelCase,					help='''RAG model retriever type''',					)
	parser.add_argument(
	    '''--index_path''',					default=_lowerCamelCase,					type=_lowerCamelCase,					help='''Path to the retrieval index''',					)
	parser.add_argument('''--n_docs''',					default=5,					type=_lowerCamelCase,					help='''Number of retrieved docs'''			)
	parser.add_argument(
	    '''--model_name_or_path''',					default=_lowerCamelCase,					type=_lowerCamelCase,					required=_lowerCamelCase,					help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''',					)
	parser.add_argument(
	    '''--eval_mode''',					choices=['''e2e''', '''retrieval'''],					default='''e2e''',					type=_lowerCamelCase,					help=(
	        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
	        ''' precision@k.'''
	    ),					)
	parser.add_argument('''--k''',					default=1,					type=_lowerCamelCase,					help='''k for the precision@k calculation'''			)
	parser.add_argument(
	    '''--evaluation_set''',					default=_lowerCamelCase,					type=_lowerCamelCase,					required=_lowerCamelCase,					help='''Path to a file containing evaluation samples''',					)
	parser.add_argument(
	    '''--gold_data_path''',					default=_lowerCamelCase,					type=_lowerCamelCase,					required=_lowerCamelCase,					help='''Path to a tab-separated file with gold samples''',					)
	parser.add_argument(
	    '''--gold_data_mode''',					default='''qa''',					type=_lowerCamelCase,					choices=['''qa''', '''ans'''],					help=(
	        '''Format of the gold data file'''
	        '''qa - a single line in the following format: question [tab] answer_list'''
	        '''ans - a single line of the gold file contains the expected answer string'''
	    ),					)
	parser.add_argument(
	    '''--predictions_path''',					type=_lowerCamelCase,					default='''predictions.txt''',					help='''Name of the predictions file, to be stored in the checkpoints directory''',					)
	parser.add_argument(
	    '''--eval_all_checkpoints''',					action='''store_true''',					help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number''',					)
	parser.add_argument(
	    '''--eval_batch_size''',					default=8,					type=_lowerCamelCase,					help='''Batch size per GPU/CPU for evaluation.''',					)
	parser.add_argument(
	    '''--recalculate''',					help='''Recalculate predictions even if the prediction file exists''',					action='''store_true''',					)
	parser.add_argument(
	    '''--num_beams''',					default=4,					type=_lowerCamelCase,					help='''Number of beams to be used when generating answers''',					)
	parser.add_argument('''--min_length''',					default=1,					type=_lowerCamelCase,					help='''Min length of the generated answers'''			)
	parser.add_argument('''--max_length''',					default=50,					type=_lowerCamelCase,					help='''Max length of the generated answers'''			)

	parser.add_argument(
	    '''--print_predictions''',					action='''store_true''',					help='''If True, prints predictions while evaluating.''',					)
	parser.add_argument(
	    '''--print_docs''',					action='''store_true''',					help='''If True, prints docs retried while generating.''',					)
	lowerCAmelCase												=  parser.parse_args()
	lowerCAmelCase												=  torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)
	return args




def __magic_name__	(				_lowerCamelCase:		Union[str, Any]			)					->						Union[str, Any]:

	'''simple docstring'''




	lowerCAmelCase												=  {}
	if args.model_type is None:
		lowerCAmelCase												=  infer_model_type(args.model_name_or_path			)
		assert args.model_type is not None
	if args.model_type.startswith('''rag'''			):
		lowerCAmelCase												=  RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
		lowerCAmelCase												=  args.n_docs
		if args.index_name is not None:
			lowerCAmelCase												=  args.index_name
		if args.index_path is not None:
			lowerCAmelCase												=  args.index_path
	else:
		lowerCAmelCase												=  BartForConditionalGeneration

	lowerCAmelCase												=  (
	    [f.path for f in os.scandir(args.model_name_or_path			) if f.is_dir()]
	    if args.eval_all_checkpoints
	    else [args.model_name_or_path]
	)

	logger.info('''Evaluate the following checkpoints: %s''',					_lowerCamelCase			)

	lowerCAmelCase												=  get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
	lowerCAmelCase												=  evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

	for checkpoint in checkpoints:
		if os.path.exists(args.predictions_path			) and (not args.recalculate):
			logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path			)			)
			score_fn(_lowerCamelCase,					args.predictions_path,					args.gold_data_path			)
			continue

		logger.info('''***** Running evaluation for {} *****'''.format(_lowerCamelCase			)			)
		logger.info('''  Batch size = %d''',					args.eval_batch_size			)
		logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path			)			)

		if args.model_type.startswith('''rag'''			):
			lowerCAmelCase												=  RagRetriever.from_pretrained(_lowerCamelCase,					**_lowerCamelCase			)
			lowerCAmelCase												=  model_class.from_pretrained(_lowerCamelCase,					retriever=_lowerCamelCase,					**_lowerCamelCase			)
			model.retriever.init_retrieval()
		else:
			lowerCAmelCase												=  model_class.from_pretrained(_lowerCamelCase,					**_lowerCamelCase			)
		model.to(args.device			)

		with open(args.evaluation_set,					'''r'''			) as eval_file, open(args.predictions_path,					'''w'''			) as preds_file:
			lowerCAmelCase												=  []
			for line in tqdm(_lowerCamelCase			):
				questions.append(line.strip()			)
				if len(_lowerCamelCase			) == args.eval_batch_size:
					lowerCAmelCase												=  evaluate_batch_fn(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)
					preds_file.write('''\n'''.join(_lowerCamelCase			) + '''\n'''			)
					preds_file.flush()
					lowerCAmelCase												=  []
			if len(_lowerCamelCase			) > 0:
				lowerCAmelCase												=  evaluate_batch_fn(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)
				preds_file.write('''\n'''.join(_lowerCamelCase			)			)
				preds_file.flush()

			score_fn(_lowerCamelCase,					args.predictions_path,					args.gold_data_path			)


if __name__ == "__main__":
					UpperCAmelCase        =				get_args()
					main(args)




"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
					from huggingface_hub import snapshot_download
					from pyctcdecode import BeamSearchDecoderCTC

					from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
					from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
					from transformers import WavaVecaForCTC



@require_pyctcdecode
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
				lowerCAmelCase												=  dict(zip(SCREAMING_SNAKE_CASE_      ,range(len(SCREAMING_SNAKE_CASE_			)			)			)			)

				lowerCAmelCase												=  {
				    '''unk_token''': '''<unk>''',
				    '''bos_token''': '''<s>''',
				    '''eos_token''': '''</s>''',
				}
				lowerCAmelCase												=  {
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 16_000,
				    '''return_attention_mask''': False,
				    '''do_normalize''': True,
				}

				lowerCAmelCase												=  tempfile.mkdtemp()
				lowerCAmelCase												=  os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['''vocab_file''']			)
				lowerCAmelCase												=  os.path.join(self.tmpdirname      ,SCREAMING_SNAKE_CASE_			)
				with open(self.vocab_file      ,'''w'''      ,encoding='''utf-8'''			) as fp:
					fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '''\n'''			)

				with open(self.feature_extraction_file      ,'''w'''      ,encoding='''utf-8'''			) as fp:
					fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '''\n'''			)

				# load decoder from hub
				lowerCAmelCase												=  '''hf-internal-testing/ngram-beam-search-decoder'''






			def 					UpperCAmelCase							(self  :	List[str]      ,**SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.add_kwargs_tokens_map.copy()
				kwargs.update(SCREAMING_SNAKE_CASE_			)
				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Dict			)       ->	Optional[int]:





				"""simple docstring"""


				return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				shutil.rmtree(self.tmpdirname			)






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				processor.save_pretrained(self.tmpdirname			)
				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname			)

				# tokenizer
				self.assertEqual(processor.tokenizer.get_vocab()      ,tokenizer.get_vocab()			)
				self.assertIsInstance(processor.tokenizer      ,SCREAMING_SNAKE_CASE_			)

				# feature extractor
				self.assertEqual(processor.feature_extractor.to_json_string()      ,feature_extractor.to_json_string()			)
				self.assertIsInstance(processor.feature_extractor      ,SCREAMING_SNAKE_CASE_			)

				# decoder
				self.assertEqual(processor.decoder._alphabet.labels      ,decoder._alphabet.labels			)
				self.assertEqual(
				    processor.decoder.model_container[decoder._model_key]._unigram_set      ,decoder.model_container[decoder._model_key]._unigram_set      ,)
				self.assertIsInstance(processor.decoder      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM(
				    tokenizer=self.get_tokenizer()      ,feature_extractor=self.get_feature_extractor()      ,decoder=self.get_decoder()			)
				processor.save_pretrained(self.tmpdirname			)

				# make sure that error is thrown when decoder alphabet doesn't match
				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained(
				    self.tmpdirname      ,alpha=5.0      ,beta=3.0      ,score_boundary=-7.0      ,unk_score_offset=3			)

				# decoder
				self.assertEqual(processor.language_model.alpha      ,5.0			)
				self.assertEqual(processor.language_model.beta      ,3.0			)
				self.assertEqual(processor.language_model.score_boundary      ,-7.0			)
				self.assertEqual(processor.language_model.unk_score_offset      ,3			)






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_tokenizer()
				# add token to trigger raise
				tokenizer.add_tokens(['''xx''']			)
				with self.assertRaisesRegex(SCREAMING_SNAKE_CASE_      ,'''include'''			):
					WavaVecaProcessorWithLM(
					    tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=self.get_feature_extractor()      ,decoder=self.get_decoder()			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  floats_list((3, 1_000)			)

				lowerCAmelCase												=  feature_extractor(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)
				lowerCAmelCase												=  processor(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)

				for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()      ,input_processor[key].sum()      ,delta=1e-2			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  '''This is a test string'''

				lowerCAmelCase												=  processor(text=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer(SCREAMING_SNAKE_CASE_			)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]      ,encoded_processor[key]			)






			def 					UpperCAmelCase							(self  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=(2, 10, 16)      ,SCREAMING_SNAKE_CASE_  :	List[Any]=77			)       ->	Optional[int]:





				"""simple docstring"""


				np.random.seed(SCREAMING_SNAKE_CASE_			)
				return np.random.rand(*SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	int			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits(shape=(10, 16)      ,seed=13			)

				lowerCAmelCase												=  processor.decode(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  decoder.decode_beams(SCREAMING_SNAKE_CASE_			)[0]

				self.assertEqual(decoded_decoder[0]      ,decoded_processor.text			)
				self.assertEqual('''</s> <s> </s>'''      ,decoded_processor.text			)
				self.assertEqual(decoded_decoder[-2]      ,decoded_processor.logit_score			)
				self.assertEqual(decoded_decoder[-1]      ,decoded_processor.lm_score			)






			@parameterized.expand([[None], ['''fork'''], ['''spawn''']]			)
			def 					UpperCAmelCase							(self  :	int      ,SCREAMING_SNAKE_CASE_  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits()

				# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
				#       otherwise, the LM won't be available to the pool's sub-processes.
				# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
				if pool_context is None:
					lowerCAmelCase												=  processor.batch_decode(SCREAMING_SNAKE_CASE_			)
				else:
					with get_context(SCREAMING_SNAKE_CASE_			).Pool() as pool:
						lowerCAmelCase												=  processor.batch_decode(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)

				with get_context('''fork'''			).Pool() as p:
					lowerCAmelCase												=  decoder.decode_beams_batch(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  [], [], []
				for beams in decoded_beams:
					texts_decoder.append(beams[0][0]			)
					logit_scores_decoder.append(beams[0][-2]			)
					lm_scores_decoder.append(beams[0][-1]			)

				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,decoded_processor.text			)
				self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']      ,decoded_processor.text			)
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,decoded_processor.logit_score			)
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,decoded_processor.lm_score			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  15
				lowerCAmelCase												=  -20.0
				lowerCAmelCase												=  -4.0

				lowerCAmelCase												=  processor.batch_decode(
				    SCREAMING_SNAKE_CASE_      ,beam_width=SCREAMING_SNAKE_CASE_      ,beam_prune_logp=SCREAMING_SNAKE_CASE_      ,token_min_logp=SCREAMING_SNAKE_CASE_      ,)
				lowerCAmelCase												=  decoded_processor_out.text

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)

				with get_context('''fork'''			).Pool() as pool:
					lowerCAmelCase												=  decoder.decode_beams_batch(
					    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,beam_width=SCREAMING_SNAKE_CASE_      ,beam_prune_logp=SCREAMING_SNAKE_CASE_      ,token_min_logp=SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  [d[0][0] for d in decoded_decoder_out]
				lowerCAmelCase												=  [d[0][2] for d in decoded_decoder_out]
				lowerCAmelCase												=  [d[0][3] for d in decoded_decoder_out]

				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']      ,SCREAMING_SNAKE_CASE_			)

				self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_      ,decoded_processor_out.logit_score			)			)
				self.assertTrue(np.allclose([-20.0_54, -18.4_47]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-3			)			)

				self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_      ,decoded_processor_out.lm_score			)			)
				self.assertTrue(np.allclose([-15.5_54, -13.94_74]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-3			)			)






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  2.0
				lowerCAmelCase												=  5.0
				lowerCAmelCase												=  -20.0
				lowerCAmelCase												=  True

				lowerCAmelCase												=  processor.batch_decode(
				    SCREAMING_SNAKE_CASE_      ,alpha=SCREAMING_SNAKE_CASE_      ,beta=SCREAMING_SNAKE_CASE_      ,unk_score_offset=SCREAMING_SNAKE_CASE_      ,lm_score_boundary=SCREAMING_SNAKE_CASE_      ,)
				lowerCAmelCase												=  decoded_processor_out.text

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)
				decoder.reset_params(
				    alpha=SCREAMING_SNAKE_CASE_      ,beta=SCREAMING_SNAKE_CASE_      ,unk_score_offset=SCREAMING_SNAKE_CASE_      ,lm_score_boundary=SCREAMING_SNAKE_CASE_      ,)

				with get_context('''fork'''			).Pool() as pool:
					lowerCAmelCase												=  decoder.decode_beams_batch(
					    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  [d[0][0] for d in decoded_decoder_out]

				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  processor.decoder.model_container[processor.decoder._model_key]
				self.assertEqual(lm_model.alpha      ,2.0			)
				self.assertEqual(lm_model.beta      ,5.0			)
				self.assertEqual(lm_model.unk_score_offset      ,-20.0			)
				self.assertEqual(lm_model.score_boundary      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)

				lowerCAmelCase												=  processor.decoder.model_container[processor.decoder._model_key]
				lowerCAmelCase												=  Path(language_model._kenlm_model.path.decode('''utf-8'''			)			).parent.parent.absolute()

				lowerCAmelCase												=  os.listdir(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  ['''alphabet.json''', '''language_model''']

				downloaded_decoder_files.sort()
				expected_decoder_files.sort()

				# test that only decoder relevant files from
				# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
				# are downloaded and none of the rest (e.g. README.md, ...)
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  snapshot_download('''hf-internal-testing/processor_with_lm'''			)

				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  processor.decoder.model_container[processor.decoder._model_key]
				lowerCAmelCase												=  Path(language_model._kenlm_model.path.decode('''utf-8'''			)			).parent.parent.absolute()

				lowerCAmelCase												=  os.listdir(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  os.listdir(SCREAMING_SNAKE_CASE_			)

				local_decoder_files.sort()
				expected_decoder_files.sort()

				# test that both decoder form hub and local files in cache are the same
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)
				lowerCAmelCase												=  AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''			)

				lowerCAmelCase												=  floats_list((3, 1_000)			)

				lowerCAmelCase												=  processor_wavaveca(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)
				lowerCAmelCase												=  processor_auto(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)

				for key in input_wavaveca.keys():
					self.assertAlmostEqual(input_wavaveca[key].sum()      ,input_auto[key].sum()      ,delta=1e-2			)

				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  processor_wavaveca.batch_decode(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  processor_auto.batch_decode(SCREAMING_SNAKE_CASE_			)

				self.assertListEqual(decoded_wavaveca.text      ,decoded_auto.text			)






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				self.assertListEqual(
				    processor.model_input_names      ,feature_extractor.model_input_names      ,msg='''`processor` and `feature_extractor` model input names do not match'''      ,)






			@staticmethod
			def 					UpperCAmelCase							(SCREAMING_SNAKE_CASE_  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	str			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  [d[key] for d in offsets]
				return retrieved_list






			def 					UpperCAmelCase							(self  :	Optional[Any]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)
				lowerCAmelCase												=  self._get_dummy_logits()[0]

				lowerCAmelCase												=  processor.decode(SCREAMING_SNAKE_CASE_      ,output_word_offsets=SCREAMING_SNAKE_CASE_			)
				# check Wav2Vec2CTCTokenizerOutput keys for word
				self.assertEqual(len(outputs.keys()			)      ,4			)
				self.assertTrue('''text''' in outputs			)
				self.assertTrue('''word_offsets''' in outputs			)
				self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)

				self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets''']      ,'''word'''			)			)      ,outputs.text			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']      ,'''word'''			)      ,['''<s>''', '''<s>''', '''</s>''']			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']      ,'''start_offset'''			)      ,[0, 2, 4]			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']      ,'''end_offset'''			)      ,[1, 3, 5]			)






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)
				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  processor.batch_decode(SCREAMING_SNAKE_CASE_      ,output_word_offsets=SCREAMING_SNAKE_CASE_			)

				# check Wav2Vec2CTCTokenizerOutput keys for word
				self.assertEqual(len(outputs.keys()			)      ,4			)
				self.assertTrue('''text''' in outputs			)
				self.assertTrue('''word_offsets''' in outputs			)
				self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)

				self.assertListEqual(
				    [''' '''.join(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''word'''			)			) for o in outputs['''word_offsets''']]      ,outputs.text			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]      ,'''word'''			)      ,['''<s>''', '''<s>''', '''</s>''']			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]      ,'''start_offset'''			)      ,[0, 2, 4]			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]      ,'''end_offset'''			)      ,[1, 3, 5]			)






			@slow
			@require_torch
			@require_torchaudio
			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Any:





				"""simple docstring"""


				import torch

				lowerCAmelCase												=  load_dataset('''common_voice'''      ,'''en'''      ,split='''train'''      ,streaming=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  ds.cast_column('''audio'''      ,datasets.Audio(sampling_rate=16_000			)			)
				lowerCAmelCase												=  iter(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  next(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''			)
				lowerCAmelCase												=  WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''			)

				# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
				lowerCAmelCase												=  processor(sample['''audio''']['''array''']      ,return_tensors='''pt'''			).input_values

				with torch.no_grad():
					lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_			).logits.cpu().numpy()

				lowerCAmelCase												=  processor.decode(logits[0]      ,output_word_offsets=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
				lowerCAmelCase												=  [
				    {
				        '''start_time''': d['''start_offset'''] * time_offset,
				        '''end_time''': d['''end_offset'''] * time_offset,
				        '''word''': d['''word'''],
				    }
				    for d in output['''word_offsets''']
				]

				lowerCAmelCase												=  '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

				# output words
				self.assertEqual(''' '''.join(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''word'''			)			)      ,SCREAMING_SNAKE_CASE_			)
				self.assertEqual(''' '''.join(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''word'''			)			)      ,output.text			)

				# output times
				lowerCAmelCase												=  torch.tensor(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''start_time'''			)			)
				lowerCAmelCase												=  torch.tensor(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''end_time'''			)			)

				# fmt: off
				lowerCAmelCase												=  torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]			)
				lowerCAmelCase												=  torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]			)
				# fmt: on

				self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,atol=0.01			)			)
				self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,atol=0.01			)			)


